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In this deep learning full course, you'll build your complete Python & Data Toolkit from scratch. We cover everything you need to know, from the absolute basics of Python syntax to building and training advanced Convolutional Neural Networks (CNNs).
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timestamps:
(NOTE: Use the actual timestamps from your final video)
MODULE 0: THE PYTHON & DATA TOOLKIT (00:00:00)
(00:01:15) Lesson 0.1: Python for Data Science (Variables, Lists, Dictionaries)
(00:15:30) Lesson 0.2: Pandas and NumPy Tutorial (DataFrames, Vectorization)
MODULE 1: FOUNDATIONAL SKILLS FOR AI (01:05:10)
(01:06:00) Lesson 1.1: Python Programming & OOP for Data Science
(01:25:45) Lesson 1.2: Essential Mathematics for AI (Linear Algebra, Calculus Intuition)
(01:45:10) Lesson 1.3: Advanced Data Manipulation & Cleaning
MODULE 2: CORE MACHINE LEARNING CONCEPTS (02:15:00)
(02:16:00) Lesson 2.1: Supervised Learning (Linear & Logistic Regression)
(02:40:00) Lesson 2.2: Model Evaluation (Precision, Recall, Overfitting)
(03:05:00) Lesson 2.3: Unsupervised Learning (K-Means Clustering, PCA)
MODULE 3: INTRODUCTION TO DEEP LEARNING (03:40:00)
(03:41:00) Lesson 3.1: What is a Neural Network? (Backpropagation, Activation Functions)
(04:10:00) Lesson 3.2: Build a Neural Network with TensorFlow/PyTorch
(04:45:00) Lesson 3.3: Convolutional Neural Networks (CNNs) for Image Data
MODULE 4: APPLIED AI & PROJECT WORK (05:20:00)
(05:21:00) Lesson 4.1: End-to-End Machine Learning Project Workflow
(05:50:00) Lesson 4.2: Transfer Learning with Pre-trained Models
(06:15:00) Lesson 4.3: Natural Language Processing (NLP) Basics with BERT
MODULE 5: ETHICAL AI & NEXT STEPS (06:45:00)
(06:46:00) Lesson 5.1: Responsible AI and How to Mitigate Bias in Machine Learning
(07:10:00) Lesson 5.2: Your AI Learning Roadmap
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